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PAGE 2021: COVID-19 Drugs and Vaccines
Nadege Neant

Modeling SARS-CoV-2 viral kinetics in hospitalized patients

Nadège Néant1, Guillaume Lingas1, Quentin Le Hingrat1,2, Cédric Laouénan1,3, Benoit Visseaux1,2, Jérémie Guedj1, for the French COVID cohort investigators and study group

1 Université de Paris, INSERM, IAME, Paris, France 2 AP-HP, Hôpital Bichat, Laboratoire de Virologie, Paris, France 3 AP-HP, Hôpital Bichat, Department of Epidemiology Biostatistics and Clinical Research, INSERM CIC-EC 1425, Paris, France.

Objectives: A detailed characterization of viral load kinetics and its association with disease evolution is a key not only to understand the virus pathogenesis but also to identify high-risk patients and design better treatment strategies (1–4). Moreover, the efficacy of the most commonly used antiviral drugs for the treatment of SARS-CoV-2 has not been clearly established yet. In this project we had therefore two mains objectives. Firstly, we developed a joint model of host/pathogen interaction and survival that captures the heterogeneity of observed viral patterns and evaluates the association between viral kinetics and death. Secondly, using this model, we analyzed data from hospitalized patients included in the Discovery trial and treated with either standard of care or remdesivir.

Methods: For the first objective, we analyzed death and viral kinetics for hospitalized patients, enrolled in the French Covid Cohort between February 5th and April 1st 2020, in 18 different hospitals (5). We therefore built a nonlinear mixed effect model of the within-host infection to describe viral dynamics. We then further extended this model to include survival, in order to evaluate the link between viral dynamics, comorbidities and mortality (6–8). Through simulations, we evaluated the effect of a potent antiviral treatment on viral dynamics and mortality.

Results: For the first objective, we analyzed the mortality and the virological information collected in 655 hospitalized patients (including 284 with longitudinal measurements). For the second objective, we analyzed viral load in upper and lower respiratory tract specimens for a total of 671 patients (2877 samples) and 123 patients (252 samples) respectively. The first model developed predicted a median peak viral load that coincided with symptom onset. Patients with age ≥65 y had a smaller loss rate of infected cells, leading to a delayed median time to viral clearance occurring 16 days after symptom onset as compared to 13 days in younger patients (P <10−4). In multivariate analysis, the risk factors associated with mortality were age ≥65 y, male gender, and presence of chronic pulmonary disease (hazard ratio [HR] >2.0). Using a joint model, viral dynamics after hospital admission was found an independent predictor of mortality (HR = 1.31, P <10−3). Finally, we used our model to simulate the effects of effective pharmacological interventions on time to viral clearance and mortality. A potent treatment able to reduce viral production by 90% upon hospital admission would shorten the time to viral clearance by 2.0 and 2.9 days in patients of age <65 y and ≥65 y, respectively. Assuming that the association between viral dynamics and mortality would remain similar to the one observed in our population, this could translate into a reduction of mortality from 19 to 14% in patients of age ≥65 y with risk factors. We will also present these results in perspective those obtained from patients from the Discovery trial that evaluated antiviral treatments.

Conclusion: Our results have shown that the predicted time to viral clearance was slower in patients age 65 or older than in patients younger than 65 and that viral dynamics after hospital admission were significantly associated with mortality. Our results demonstrated the importance of viral dynamics to identify at-risk patients and suggested that strategies aiming to accelerate viral clearance could decrease mortality in these patients.

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[3] A. S. Perelson, A. U. Neumann, M. Markowitz, J. M. Leonard, D. D. Ho, HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral  generation time. Science 271, 1582–1586 (1996).
[4] A. U. Neumann, et al., Hepatitis C viral dynamics in vivo and the antiviral efficacy of interferon-alpha  therapy. Science 282, 103–107 (1998).
[5] Y. Yazdanpanah, F. C. cohort study group, Impact on disease mortality of clinical, biological and virological characteristics at hospital admission and over time in COVID-19 patients. J. Med. Virol. n/a (2020).
[6] V. Madelain, et al., Ebola viral dynamics in nonhuman primates provides insights into virus immuno-pathogenesis and antiviral strategies. Nat. Commun. 9, 1–11 (2018).
[7] C. Tardivon, et al., Association Between Tumor Size Kinetics and Survival in Patients With Urothelial Carcinoma Treated With Atezolizumab: Implication for Patient Follow-Up. Clin. Pharmacol. Ther. 106, 810–820 (2019).
[8] S. Desmée, F. Mentré, C. Veyrat-Follet, J. Guedj, Nonlinear Mixed-Effect Models for Prostate-Specific Antigen Kinetics and Link with Survival in the Context of Metastatic Prostate Cancer: a Comparison by Simulation of Two-Stage and Joint Approaches. AAPS J. 17, 691–699 (2015).

Reference: PAGE 29 (2021) Abstr 9674 [www.page-meeting.org/?abstract=9674]
Oral: COVID-19 Drugs and Vaccines